دانلود رایگان مقاله انگلیسی برآورد و تجزیه و تحلیل بهره وری گردشگری - الزویر 2018

abstract

This paper estimates a total factor productivity index that allows for a rich decomposition of productivity in the tourism industry. We account for two important characteristics: First, the heterogeneity between multiple tourism destinations, and second, the potential endogeneity in inputs. Importantly we develop our index at the macro level, focusing on cross-country comparisons. Using the Bayesian approach, we test the performance of the model across various priors. We rank tourism destinations based on their tourism productivity and discuss the main sources of productivity growth. We also provide long-run productivity measures and discuss the importance of distinguishing between short-run and long-run productivity measures for future performance improvement strategies.

7. Concluding remarks

We introduced in this paper several important contributions to the tourism literature. First, we estimated a more robust productivity index that accounts for unobserved heterogeneity as well as the classical endogeneity problem in the estimation of input distance functions. Second, we provided a richer decomposition of productivity growth into three important components (input change, output change and frontier change). Third, we derived both short term and long-term productivity measures, providing hence some richer information for policy formulation in the tourism industry. Fourth, we provided measures of efficiency for each tourism destination, and applied the new methods to a rich of sample of leading tourism destinations and provided aggregate and individual country results. As mentioned, most existing studies in the area have focused only on one destination, or specific regions within one specific destination. Fifth, and finally, we measured productivity for the first time in this area using the Bayesian approach. The advanced assumption we impose on our model gives rise to a complicated statistical estimation problem which can be addressed successfully via Bayesian methods based on Sequential Monte Carlo/Particle Filtering (SMC/PF). We tested the performance of the model across various priors and also tested whether the instruments we selected for the reduced form are strong enough and proper.